Determination of alteration genesis and quantitative relationship between alteration and geochemical anomaly using support vector machines

Document Type : Research Paper

Authors

1 Department of mining engineering, University of Gonabab, Iran

2 School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

In this research, support vector machine (SVM) as a supervised classification method has been used to explore the relationship between the geochemical anomaly and the surface alterations quantitatively in the Tanurcheh mineralization area. The Tanurcheh area has been located in the Khorasan Razavi province, Iran. This area has been considered as a high potential region for Cu and Au mineralization. The different mineralization processes of Au and Cu have unclearly been intertwined in this area and have created extreme surface alterations.

Determination of the major origin of mineralization that has created strong alterations in this area is an important issue that can be addressed using a new proposed scenario. The relationship between the geochemical distribution map and the alteration zone was mathematically calculated using the proposed approach and then the geochemical anomaly map was predicted based on the alteration zones as an innovative achievement.

In this paper, the Au and Cu geochemical data were divided into three classes, namely background, regional anomaly and local anomaly using the probability plot method. Two threshold values for Cu (70 and 300 PPM) and Au (0.13 and 0.4 PPM) were obtained by the probability plot method. Then the SVM was utilized to classify the geochemical samples using the ASTER images based on these obtained thresholds. The ASTER 14-band images were used as features in this classification. Using this novel scenario, the relationships between the Au and Cu mineralization processes with the intensity of alterations were determined and therefore the origin of these alteration zones was clarified. The SVM classification indices of correct classification rate (CCR) and confusion matrix demonstrate the main origin of alterations is related to the Cu mineralization process in this area. The CCR indices obtained based on the Au and Cu thresholds are 0.66 and 0.85 respectively. It demonstrates the intensity of alterations has more been affected by the Cu mineralization process and there is a relatively good relationship between the alteration zone and the Cu geochemical distribution map. Finally, the geochemical anomaly and background maps were properly predicted using the SVM and the ASTER bands. This paper shows the new application of SVM as a powerful tool for the interpretation of geochemical anomaly and the intensity of alteration.

Keywords

Main Subjects


  1. [1] Zuo, R.(2017). Machine Learning of Mineralization-Related Geochemical  Anomalies:  A  Review  of  Potential     Methods, Natural Resources Research

    [2] Chen, Y., Lu, L., & Li, X. (2014). Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly. Journal of Geochemical Exploration, 140, 56–63.

    [3] O’Brien, J. J., Spry, P. G., Nettleton, D., Xu, R., &Teale, G. S. (2015). Using random forests to distinguish gahnite compositions as an exploration guide to Broken Hill-type Pb– Zn–Ag deposits in the Broken Hill domain, Australia. Journal of Geochemical Exploration, 49, 74–86.

    [4] Gonbadi, A. B., Tabatabaei, S. H., & Carranza, E. J. M. (2015). Supervised geochemical anomaly detection by pattern recognition. Journal of Geochemical Exploration, 157, 81–91.

    [5] Kirkwood, C., Cave, M., Beamish, D., Grebby, S., & Ferreira, A. (2016). A machine learning approach to geochemical mapping. Journal of Geochemical Exploration, 167, 49–61.

    [6] Zhao, J., Chen, S., &Zuo, R. (2016). Identifying geochemical anomalies associated with Au–Cu mineralization using multifractal and artificial neural network models in the Ningqiang district, Shaanxi, China. Journal of Geochemical Exploration, 164, 54–64.

    [7] Xiong, Y., Zuo, R. (2016). Recognition of geochemical anomalies using a deep autoencoder network. Computers & Geosciences, 86, 75–82.

    [8] Chen, Y., & Wu, W. (2017). Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data. Geochemistry: Exploration, Environment, Analysis, 17, 231–238.

    [9] Zuo, R. and Xiong, Y. (2017). Big Data Analytics of Identifying Geochemical Anomalies Supported by Machine Learning Methods. Natural Resources Research, pp.1-9.

    [10] Vapnik, V. (1995). The Nature of Statistical Learning Theory: Springer, New York, 314 p.

    [11] Vapnik, V. (1998). Statistical Learning Theory: Wiley, New York, 736 p.

    [12] Twarakavi, N. K. C., Misra, D., &Bandopadhyay, S. (2006). Prediction of arsenic in bedrock-derived stream sediments at a gold mine site under conditions of sparse data. Natural Resources Research, 15, 15–26.

    [13]  Zarmehr Company, (2004). exploration report of Tnurcheh Au – Cu mineralization area.

    [14] Salimi, A., Ziaii, M., HosseinjaniZadeh, M., Amiri, A. and Karimpouli, S. (2015). High performance of the support vector machine in classifying hyperspectral data using a limited dataset. Int. Journal of Mining & Geo-Engineering, 49(2), pp.253-268.

    [15] Lu, J., & Zhang, E. (2007). Gait recognition for human identification based on ICA and fuzzy SVM through multiple views fusion. Pattern Recognition Letters, 28(16), 2401-2411.

    [16] Thodoridis, S and Koutroumbas, K (2009) ‘’Pattern recognition’’, Fourth Edition, Elsevier. [17]  Wu, Y., Yang, X., Plaza, A., Qiao, F., Gao, L., Zhang, B., and  Cui,

    1. (2016). Approximate Computing of Remotely Sensed Data: SVM Hyperspectral Image Classification as a Case Study. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(12), pp.5806-5818.

    [18] Chang, C. (2007). Hyperspectral Data Exploitation, Theory, and Applications. Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

    [19] Camps-Valls, G. and Bruzzone, L.(2005). Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 43(6), pp.1351-1362.

    [20] Mahdiyanfar, H. (2020). Prediction of economic potential of deep blind mineralization by Fourier transform of a geochemical dataset. Periodico di Mineralogia, 90(1).

    [21] Mahdiyanfar, H. (2020). Identification of deep blind mineralization and dispersed zones using geochemical frequency anomaly in comparison with    Zonality method. Journal of Analytical and Numerical Methods in Mining Engineering, 10(23), 1-16.

    [22] Fawcett, T. (2003). Notes and Practical Considerations for Data Mining Researchers, Intelligent enterprise Technologies Laboratory, HP Labs Palo Alto. HPL-2003-4

    [23] Swets, J.A. (2014). Signal detection theory and ROC analysis in psychology and diagnostics: Collected papers. Psychology Press